Overview

Dataset statistics

Number of variables11
Number of observations8757
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory821.0 KiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

green_energy_HU is highly overall correlated with Load_HU_Load and 1 other fieldsHigh correlation
green_energy_IT is highly overall correlated with green_energy_PO and 3 other fieldsHigh correlation
green_energy_PO is highly overall correlated with green_energy_IT and 4 other fieldsHigh correlation
green_energy_SE is highly overall correlated with green_energy_PO and 2 other fieldsHigh correlation
Load_HU_Load is highly overall correlated with green_energy_HU and 1 other fieldsHigh correlation
Load_IT_Load is highly overall correlated with green_energy_IT and 3 other fieldsHigh correlation
Load_PO_Load is highly overall correlated with green_energy_HU and 1 other fieldsHigh correlation
Load_SE_Load is highly overall correlated with green_energy_IT and 4 other fieldsHigh correlation
Load_SP_Load is highly overall correlated with green_energy_IT and 4 other fieldsHigh correlation

Reproduction

Analysis started2023-11-18 23:29:53.600053
Analysis finished2023-11-18 23:30:19.273355
Duration25.67 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

green_energy_HU
Real number (ℝ)

HIGH CORRELATION 

Distinct6737
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14350.508
Minimum5699
Maximum28137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:19.437354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5699
5-th percentile8411.6
Q110488
median13770
Q317550
95-th percentile22578.2
Maximum28137
Range22438
Interquartile range (IQR)7062

Descriptive statistics

Standard deviation4513.6594
Coefficient of variation (CV)0.31452959
Kurtosis-0.58988584
Mean14350.508
Median Absolute Deviation (MAD)3462
Skewness0.49630511
Sum1.256674 × 108
Variance20373121
MonotonicityNot monotonic
2023-11-19T00:30:19.716640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8840 6
 
0.1%
8979 5
 
0.1%
10238 5
 
0.1%
15152 5
 
0.1%
9177 5
 
0.1%
17678 5
 
0.1%
9809 5
 
0.1%
12403 5
 
0.1%
14490 5
 
0.1%
9980 5
 
0.1%
Other values (6727) 8706
99.4%
ValueCountFrequency (%)
5699 1
< 0.1%
5746 1
< 0.1%
5809 1
< 0.1%
5834 1
< 0.1%
5848 1
< 0.1%
5869 1
< 0.1%
5882 1
< 0.1%
5892 1
< 0.1%
5933 1
< 0.1%
5940 1
< 0.1%
ValueCountFrequency (%)
28137 1
< 0.1%
28020 1
< 0.1%
27870 1
< 0.1%
27694 1
< 0.1%
27536 1
< 0.1%
27188 1
< 0.1%
27145 1
< 0.1%
27087 1
< 0.1%
27077 1
< 0.1%
26909 1
< 0.1%

green_energy_IT
Real number (ℝ)

HIGH CORRELATION 

Distinct7473
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28594.607
Minimum12960
Maximum47439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:19.985491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12960
5-th percentile17637
Q122739
median28243
Q334100
95-th percentile40791.2
Maximum47439
Range34479
Interquartile range (IQR)11361

Descriptive statistics

Standard deviation7223.1188
Coefficient of variation (CV)0.25260424
Kurtosis-0.8853411
Mean28594.607
Median Absolute Deviation (MAD)5686
Skewness0.16589367
Sum2.5040297 × 108
Variance52173445
MonotonicityNot monotonic
2023-11-19T00:30:20.279781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24614 4
 
< 0.1%
32190 4
 
< 0.1%
31050 4
 
< 0.1%
22662 4
 
< 0.1%
28010 4
 
< 0.1%
39110 4
 
< 0.1%
24567 4
 
< 0.1%
27614 4
 
< 0.1%
35579 4
 
< 0.1%
24145 4
 
< 0.1%
Other values (7463) 8717
99.5%
ValueCountFrequency (%)
12960 1
< 0.1%
13008 1
< 0.1%
13124 1
< 0.1%
13256 1
< 0.1%
13266 1
< 0.1%
13468 1
< 0.1%
13695 1
< 0.1%
13779 1
< 0.1%
13819 1
< 0.1%
13844 1
< 0.1%
ValueCountFrequency (%)
47439 1
< 0.1%
46984 1
< 0.1%
46783 1
< 0.1%
46459 1
< 0.1%
46386 1
< 0.1%
46291 1
< 0.1%
46201 1
< 0.1%
45916 1
< 0.1%
45914 1
< 0.1%
45720 1
< 0.1%

green_energy_PO
Real number (ℝ)

HIGH CORRELATION 

Distinct5910
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18572.007
Minimum11694
Maximum25205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:20.563946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum11694
5-th percentile14110
Q116395
median18740
Q320551
95-th percentile23210
Maximum25205
Range13511
Interquartile range (IQR)4156

Descriptive statistics

Standard deviation2758.6082
Coefficient of variation (CV)0.14853582
Kurtosis-0.73335658
Mean18572.007
Median Absolute Deviation (MAD)2057
Skewness-0.0075695088
Sum1.6263507 × 108
Variance7609919.5
MonotonicityNot monotonic
2023-11-19T00:30:20.872735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20402 6
 
0.1%
20760 6
 
0.1%
17566 6
 
0.1%
19650 6
 
0.1%
20842 5
 
0.1%
20498 5
 
0.1%
19947 5
 
0.1%
18232 5
 
0.1%
17288 5
 
0.1%
17173 5
 
0.1%
Other values (5900) 8703
99.4%
ValueCountFrequency (%)
11694 1
< 0.1%
11878 1
< 0.1%
11883 1
< 0.1%
11903 1
< 0.1%
11917 1
< 0.1%
11934 1
< 0.1%
12071 1
< 0.1%
12131 1
< 0.1%
12138 1
< 0.1%
12150 1
< 0.1%
ValueCountFrequency (%)
25205 1
< 0.1%
25193 1
< 0.1%
25122 1
< 0.1%
25097 1
< 0.1%
25079 1
< 0.1%
25024 2
< 0.1%
25012 1
< 0.1%
25010 1
< 0.1%
25008 1
< 0.1%
24998 1
< 0.1%

green_energy_SE
Real number (ℝ)

HIGH CORRELATION 

Distinct5603
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18492.484
Minimum12697
Maximum31916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:21.166337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12697
5-th percentile15007.2
Q116451
median17881
Q320549
95-th percentile23200
Maximum31916
Range19219
Interquartile range (IQR)4098

Descriptive statistics

Standard deviation2596.3191
Coefficient of variation (CV)0.14039861
Kurtosis-0.49990616
Mean18492.484
Median Absolute Deviation (MAD)1917
Skewness0.49601815
Sum1.6193868 × 108
Variance6740873.1
MonotonicityNot monotonic
2023-11-19T00:30:21.481647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17503 7
 
0.1%
16557 7
 
0.1%
16730 7
 
0.1%
20850 7
 
0.1%
17313 6
 
0.1%
16481 6
 
0.1%
17243 6
 
0.1%
16422 6
 
0.1%
16924 6
 
0.1%
20480 6
 
0.1%
Other values (5593) 8693
99.3%
ValueCountFrequency (%)
12697 1
< 0.1%
12812 1
< 0.1%
12817 1
< 0.1%
12853 1
< 0.1%
12858 1
< 0.1%
13005 1
< 0.1%
13016 1
< 0.1%
13095 1
< 0.1%
13126 1
< 0.1%
13156 1
< 0.1%
ValueCountFrequency (%)
31916 1
< 0.1%
31770 1
< 0.1%
26026 1
< 0.1%
25902 1
< 0.1%
25823 1
< 0.1%
25791 1
< 0.1%
25789 1
< 0.1%
25785 1
< 0.1%
25783 1
< 0.1%
25751 1
< 0.1%

green_energy_SP
Real number (ℝ)

Distinct8021
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87589.518
Minimum19424
Maximum220268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:21.773321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum19424
5-th percentile24235.6
Q131633
median87108
Q3132488
95-th percentile180098.4
Maximum220268
Range200844
Interquartile range (IQR)100855

Descriptive statistics

Standard deviation54322.733
Coefficient of variation (CV)0.62019673
Kurtosis-1.1535463
Mean87589.518
Median Absolute Deviation (MAD)54289
Skewness0.34848167
Sum7.6702141 × 108
Variance2.9509593 × 109
MonotonicityNot monotonic
2023-11-19T00:30:22.084284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89464 4
 
< 0.1%
86520 3
 
< 0.1%
105740 3
 
< 0.1%
127492 3
 
< 0.1%
29839 3
 
< 0.1%
31400 3
 
< 0.1%
100076 3
 
< 0.1%
27045 3
 
< 0.1%
30683 3
 
< 0.1%
112116 3
 
< 0.1%
Other values (8011) 8726
99.6%
ValueCountFrequency (%)
19424 1
< 0.1%
19475 1
< 0.1%
19797 1
< 0.1%
19894 1
< 0.1%
20055 1
< 0.1%
20072 1
< 0.1%
20077 1
< 0.1%
20086 1
< 0.1%
20116 1
< 0.1%
20173 1
< 0.1%
ValueCountFrequency (%)
220268 1
< 0.1%
220236 1
< 0.1%
217512 1
< 0.1%
215540 1
< 0.1%
212584 1
< 0.1%
212536 1
< 0.1%
212440 1
< 0.1%
212392 1
< 0.1%
212384 1
< 0.1%
212208 1
< 0.1%

Load_HU_Load
Real number (ℝ)

HIGH CORRELATION 

Distinct7925
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45839.758
Minimum16419
Maximum88069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:22.395793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16419
5-th percentile27688.4
Q133623
median44226
Q355917
95-th percentile70793.2
Maximum88069
Range71650
Interquartile range (IQR)22294

Descriptive statistics

Standard deviation14052.621
Coefficient of variation (CV)0.30655968
Kurtosis-0.69555916
Mean45839.758
Median Absolute Deviation (MAD)11031
Skewness0.44316096
Sum4.0141876 × 108
Variance1.9747617 × 108
MonotonicityNot monotonic
2023-11-19T00:30:22.694423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30730 4
 
< 0.1%
52996 4
 
< 0.1%
38438 3
 
< 0.1%
33982 3
 
< 0.1%
28707 3
 
< 0.1%
33750 3
 
< 0.1%
38673 3
 
< 0.1%
31212 3
 
< 0.1%
35168 3
 
< 0.1%
32835 3
 
< 0.1%
Other values (7915) 8725
99.6%
ValueCountFrequency (%)
16419 1
< 0.1%
16610 1
< 0.1%
16950 1
< 0.1%
17020 1
< 0.1%
17700 1
< 0.1%
17702 1
< 0.1%
17711 1
< 0.1%
17880 1
< 0.1%
18035 1
< 0.1%
18290 1
< 0.1%
ValueCountFrequency (%)
88069 1
< 0.1%
87932 1
< 0.1%
87134 1
< 0.1%
85473 1
< 0.1%
84428 1
< 0.1%
84412 1
< 0.1%
83955 1
< 0.1%
83929 1
< 0.1%
83527 1
< 0.1%
83440 1
< 0.1%

Load_IT_Load
Real number (ℝ)

HIGH CORRELATION 

Distinct7425
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32679.652
Minimum16599
Maximum51761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:22.985683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16599
5-th percentile21942.4
Q126505
median32187
Q338351
95-th percentile44665.6
Maximum51761
Range35162
Interquartile range (IQR)11846

Descriptive statistics

Standard deviation7235.692
Coefficient of variation (CV)0.22141276
Kurtosis-0.93853972
Mean32679.652
Median Absolute Deviation (MAD)5915
Skewness0.16543986
Sum2.8617571 × 108
Variance52355238
MonotonicityNot monotonic
2023-11-19T00:30:23.293169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24798 5
 
0.1%
34720 4
 
< 0.1%
27995 4
 
< 0.1%
35945 4
 
< 0.1%
27169 4
 
< 0.1%
42476 4
 
< 0.1%
30417 4
 
< 0.1%
39209 4
 
< 0.1%
30006 4
 
< 0.1%
32168 4
 
< 0.1%
Other values (7415) 8716
99.5%
ValueCountFrequency (%)
16599 1
< 0.1%
16615 1
< 0.1%
16730 1
< 0.1%
16850 1
< 0.1%
16997 1
< 0.1%
17007 1
< 0.1%
17050 1
< 0.1%
17187 1
< 0.1%
17231 1
< 0.1%
17294 1
< 0.1%
ValueCountFrequency (%)
51761 1
< 0.1%
51517 1
< 0.1%
51369 1
< 0.1%
51106 1
< 0.1%
51056 1
< 0.1%
50802 1
< 0.1%
50738 1
< 0.1%
50608 1
< 0.1%
50287 1
< 0.1%
50252 1
< 0.1%

Load_PO_Load
Real number (ℝ)

HIGH CORRELATION 

Distinct8596
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220395
Minimum102899
Maximum392428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:23.594106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum102899
5-th percentile127660.8
Q1165647
median207161
Q3274800
95-th percentile338291
Maximum392428
Range289529
Interquartile range (IQR)109153

Descriptive statistics

Standard deviation67755.678
Coefficient of variation (CV)0.30742837
Kurtosis-0.87665655
Mean220395
Median Absolute Deviation (MAD)51933
Skewness0.40545937
Sum1.9299991 × 109
Variance4.5908319 × 109
MonotonicityNot monotonic
2023-11-19T00:30:23.920997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180750 3
 
< 0.1%
190703 3
 
< 0.1%
138927 3
 
< 0.1%
310548 3
 
< 0.1%
161140 3
 
< 0.1%
192179 2
 
< 0.1%
313159 2
 
< 0.1%
153308 2
 
< 0.1%
202251 2
 
< 0.1%
165360 2
 
< 0.1%
Other values (8586) 8732
99.7%
ValueCountFrequency (%)
102899 1
< 0.1%
104229 1
< 0.1%
104887 1
< 0.1%
105114 2
< 0.1%
105194 1
< 0.1%
105231 1
< 0.1%
105348 1
< 0.1%
105415 1
< 0.1%
105532 1
< 0.1%
105799 1
< 0.1%
ValueCountFrequency (%)
392428 1
< 0.1%
392096 1
< 0.1%
390163 1
< 0.1%
388520 1
< 0.1%
387818 1
< 0.1%
387301 1
< 0.1%
386849 1
< 0.1%
386737 1
< 0.1%
385703 1
< 0.1%
385159 1
< 0.1%

Load_SE_Load
Real number (ℝ)

HIGH CORRELATION 

Distinct6075
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19681.103
Minimum11824
Maximum27211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:24.232175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum11824
5-th percentile14575.8
Q117023
median19849
Q321998
95-th percentile25024.4
Maximum27211
Range15387
Interquartile range (IQR)4975

Descriptive statistics

Standard deviation3164.1443
Coefficient of variation (CV)0.16077068
Kurtosis-0.85789722
Mean19681.103
Median Absolute Deviation (MAD)2417
Skewness-0.0074640131
Sum1.7234742 × 108
Variance10011809
MonotonicityNot monotonic
2023-11-19T00:30:24.554284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20912 6
 
0.1%
18189 6
 
0.1%
16313 6
 
0.1%
19017 6
 
0.1%
22580 6
 
0.1%
21700 6
 
0.1%
21948 5
 
0.1%
20877 5
 
0.1%
21147 5
 
0.1%
17702 5
 
0.1%
Other values (6065) 8701
99.4%
ValueCountFrequency (%)
11824 1
< 0.1%
11924 1
< 0.1%
12073 1
< 0.1%
12200 1
< 0.1%
12307 1
< 0.1%
12319 1
< 0.1%
12453 1
< 0.1%
12583 1
< 0.1%
12612 1
< 0.1%
12684 1
< 0.1%
ValueCountFrequency (%)
27211 1
< 0.1%
27159 1
< 0.1%
26942 1
< 0.1%
26927 1
< 0.1%
26911 1
< 0.1%
26873 1
< 0.1%
26869 1
< 0.1%
26850 1
< 0.1%
26843 2
< 0.1%
26824 1
< 0.1%

Load_SP_Load
Real number (ℝ)

HIGH CORRELATION 

Distinct2718
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3919.1688
Minimum2233
Maximum5834
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:24.846668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2233
5-th percentile2818
Q13392
median3893
Q34420
95-th percentile5151.2
Maximum5834
Range3601
Interquartile range (IQR)1028

Descriptive statistics

Standard deviation703.08893
Coefficient of variation (CV)0.17939746
Kurtosis-0.65738298
Mean3919.1688
Median Absolute Deviation (MAD)515
Skewness0.14984163
Sum34320161
Variance494334.05
MonotonicityNot monotonic
2023-11-19T00:30:25.143517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3661 12
 
0.1%
3785 12
 
0.1%
4294 11
 
0.1%
3384 11
 
0.1%
3699 10
 
0.1%
2926 10
 
0.1%
3903 10
 
0.1%
4374 10
 
0.1%
3639 10
 
0.1%
4071 10
 
0.1%
Other values (2708) 8651
98.8%
ValueCountFrequency (%)
2233 1
< 0.1%
2311 1
< 0.1%
2316 1
< 0.1%
2324 1
< 0.1%
2338 1
< 0.1%
2340 1
< 0.1%
2349 1
< 0.1%
2371 1
< 0.1%
2394 1
< 0.1%
2404 1
< 0.1%
ValueCountFrequency (%)
5834 1
< 0.1%
5818 1
< 0.1%
5817 1
< 0.1%
5805 1
< 0.1%
5794 1
< 0.1%
5756 1
< 0.1%
5750 1
< 0.1%
5685 1
< 0.1%
5677 1
< 0.1%
5675 1
< 0.1%

Result
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5348864
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.8 KiB
2023-11-19T00:30:25.407302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8835039
Coefficient of variation (CV)0.52096894
Kurtosis-1.2265017
Mean5.5348864
Median Absolute Deviation (MAD)3
Skewness-0.026908186
Sum48469
Variance8.3145948
MonotonicityNot monotonic
2023-11-19T00:30:25.623099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 906
10.3%
10 898
10.3%
7 897
10.2%
1 890
10.2%
9 883
10.1%
8 882
10.1%
2 876
10.0%
5 868
9.9%
3 839
9.6%
4 818
9.3%
ValueCountFrequency (%)
1 890
10.2%
2 876
10.0%
3 839
9.6%
4 818
9.3%
5 868
9.9%
6 906
10.3%
7 897
10.2%
8 882
10.1%
9 883
10.1%
10 898
10.3%
ValueCountFrequency (%)
10 898
10.3%
9 883
10.1%
8 882
10.1%
7 897
10.2%
6 906
10.3%
5 868
9.9%
4 818
9.3%
3 839
9.6%
2 876
10.0%
1 890
10.2%

Interactions

2023-11-19T00:30:16.605029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:54.049356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:56.273950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:58.523349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:00.783269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:03.039122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:05.445532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:07.661147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:09.893849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:12.133680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:14.368627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:16.784966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:54.306473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:56.463588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:58.721058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:00.981764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:03.227623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:05.629495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:07.843560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:10.075301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:12.330904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:14.547787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:16.970587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:54.488786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:56.670563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:58.930786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:01.187597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:03.429365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:05.828922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:08.034379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:10.282948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:12.534318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:14.751748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:17.164527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:54.683725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:56.908232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:59.139929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:01.397103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:03.651822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:06.022120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:08.240457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:10.487970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:12.726905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:14.949205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:17.366914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:54.897235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:57.132483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:59.357427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:01.607859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:03.878484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:06.242482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:08.444562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:10.737386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:12.950042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:15.161245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:17.574637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:55.095833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:57.351322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:59.585054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:01.815671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:04.117054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:06.457951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:08.657591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:10.943377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:13.163479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:15.369610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:17.770651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:55.283893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:57.543680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:59.783365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:02.028590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:04.341605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:06.656513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:08.889610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:11.144320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:13.366858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:15.589520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:17.971904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:55.500886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:57.740620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:59.991276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:02.228721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:04.556426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:06.865941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:09.099263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:11.358035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:13.574085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:15.811657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:18.159496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:55.690161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:57.931998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:00.185701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:02.433558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:04.768873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:07.067200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:09.307371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:11.551465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:13.776415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:16.010143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:18.349700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:55.883112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:58.130782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:00.390584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:02.649298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:04.984828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:07.271214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:09.510575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:11.753849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:13.982062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:16.205490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:18.538586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:56.083952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:29:58.332927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:00.605856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:02.856466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:05.242713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:07.474627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:09.713323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:11.953417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:14.190243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-19T00:30:16.412831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-19T00:30:25.809862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
green_energy_HUgreen_energy_ITgreen_energy_POgreen_energy_SEgreen_energy_SPLoad_HU_LoadLoad_IT_LoadLoad_PO_LoadLoad_SE_LoadLoad_SP_LoadResult
green_energy_HU1.0000.3740.3750.2440.2390.7150.3390.9070.3470.355-0.010
green_energy_IT0.3741.0000.7970.3240.2400.2140.9510.4430.8310.703-0.005
green_energy_PO0.3750.7971.0000.592-0.0130.2790.7720.4940.9640.862-0.010
green_energy_SE0.2440.3240.5921.000-0.4020.2660.2840.3360.5470.674-0.011
green_energy_SP0.2390.240-0.013-0.4021.0000.2480.2460.2400.070-0.082-0.004
Load_HU_Load0.7150.2140.2790.2660.2481.0000.2170.8590.3020.262-0.011
Load_IT_Load0.3390.9510.7720.2840.2460.2171.0000.4320.8190.679-0.004
Load_PO_Load0.9070.4430.4940.3360.2400.8590.4321.0000.4900.482-0.011
Load_SE_Load0.3470.8310.9640.5470.0700.3020.8190.4901.0000.855-0.012
Load_SP_Load0.3550.7030.8620.674-0.0820.2620.6790.4820.8551.000-0.015
Result-0.010-0.005-0.010-0.011-0.004-0.011-0.004-0.011-0.012-0.0151.000

Missing values

2023-11-19T00:30:18.798144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-19T00:30:19.132750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

green_energy_HUgreen_energy_ITgreen_energy_POgreen_energy_SEgreen_energy_SPLoad_HU_LoadLoad_IT_LoadLoad_PO_LoadLoad_SE_LoadLoad_SP_LoadResult
HourlyTime
2022-01-01 00:00:00+00:0012819.014902.013984.018219.021745.040999.019756.0166143.013935.03218.09
2022-01-01 01:00:00+00:009769.014937.014037.018152.022097.029850.018685.0121975.013579.03126.04
2022-01-01 02:00:00+00:0016334.014461.013946.017626.022180.048908.018124.0198204.013397.03080.09
2022-01-01 03:00:00+00:009670.013882.013931.017910.022057.028853.018400.0118213.013364.03044.08
2022-01-01 04:00:00+00:0016006.013819.013817.018025.021892.047538.019223.0194446.013449.03130.07
2022-01-01 05:00:00+00:009500.014391.013759.018371.022154.029004.019738.0115144.013444.03238.02
2022-01-01 06:00:00+00:0015880.014858.013707.019066.022200.049446.020289.0194855.013527.03378.09
2022-01-01 07:00:00+00:009438.015834.014148.019094.024283.030454.021469.0120606.014202.03537.02
2022-01-01 08:00:00+00:0017414.016596.014196.019043.026794.051625.022735.0210658.014851.03714.01
2022-01-01 09:00:00+00:0010799.018259.014739.018850.028184.031937.023636.0132340.015643.03818.010
green_energy_HUgreen_energy_ITgreen_energy_POgreen_energy_SEgreen_energy_SPLoad_HU_LoadLoad_IT_LoadLoad_PO_LoadLoad_SE_LoadLoad_SP_LoadResult
HourlyTime
2022-12-31 13:00:00+00:009812.023112.017376.018512.087684.038338.025901.0148589.018172.04105.07
2022-12-31 14:00:00+00:0015494.024278.018285.018488.0145136.063469.026991.0245716.018805.04293.02
2022-12-31 15:00:00+00:009154.027302.017669.018514.084144.037960.029874.0149012.018612.04398.010
2022-12-31 16:00:00+00:0015163.027881.017297.018479.0134416.065196.030275.0261551.018131.04291.08
2022-12-31 17:00:00+00:009048.026746.016578.017581.080912.038708.029519.0157668.017259.03932.03
2022-12-31 18:00:00+00:0014752.024761.015511.016929.0132996.061718.027366.0251768.016138.03694.08
2022-12-31 19:00:00+00:008584.022091.014951.016638.077088.035036.024809.0141528.015211.03713.03
2022-12-31 20:00:00+00:0014318.020499.014841.016046.0127448.055210.023169.0222543.014641.03579.05
2022-12-31 21:00:00+00:008566.019463.014980.015593.074148.031730.021857.0129032.013977.03520.05
2022-12-31 22:00:00+00:0014091.017734.013687.015266.0119744.051211.020555.0205814.013272.03440.05